Graph autoencoder-based unsupervised outlier detection

Du, Xusheng; Yu, Jiong; Chu, Zheng; Jin, Lan; Chen, Jiaying

doi:10.1016/j.ins.2022.06.039

Cited by 37 publications

(6 citation statements)

References 28 publications

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“…Traffic features are effective expressions of node attributes and can be utilized for node clustering, but limited by the expressiveness of low dimension, there are shortcomings in applying original traffic features as inputs to BiKmeans algorithm. GAE is a typical model for Representation Learning based on Deep Learning, which can learn the embedding of inputs in a high-dimensional feature space [44] . Therefore, we first employed GAE for training the traffic sequence data to obtain the high-dimensional embedding of traffic features, then combined it with BiKmeans algorithm to cluster traffic nodes, which is shown in Alg.…”

Section: Methodsmentioning

confidence: 99%

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Yu,

Bao,

Shi

2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Yu,

Bao,

Shi

2023

Heliyon

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“…Graph AutoEncoder 44 (GAE) is a widely used graph neural network-based method for outlier detection. It sorts data objects in descending order and calculates outlier factors to determine outliers.…”

Section: Related Workmentioning

confidence: 99%

Bearing fault detection by using graph autoencoder and ensemble learning

Wang,

Yu,

Leng

et al. 2024

Sci Rep

Self Cite

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The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

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“…GCAE [31] applies the idea of autoencoder to graph data. An autoencoder is a neural network structure that compresses input data into a low-dimensional representation (encodes) and then restores them back to their original dimensionality (decodes).…”

Section: Comparison Dgtsd To Other Modelsmentioning

confidence: 99%

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

Choi,

Buu

2024

Electronics

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Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.

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Graph autoencoder-based unsupervised outlier detection

Cited by 37 publications

References 28 publications

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Bearing fault detection by using graph autoencoder and ensemble learning

Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection

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